With the increasing growth of large data storage and computational demand, Green Cloud Computing is known to be a broad area and hot field for research. To capitalize various IT resources, Cloud computing has produced an ultimate and impressing way to virtualize servers and data centers and to make energy efficient. The IT resources consume huge amounts of power and energy, which in turn produces shortage in energy and change in global climate. Therefore, there is a need of Green cloud computing which can produce solutions that can not only make the IT resources energy efficient but also minimize the operational costs. To solve environment related issues in the field of IT, Green IT is named to be an important step. It includes a huge number of focus areas for instance to provide proper management of power, virtualization of servers, design of data centers, recycling methods, eco-labeling, environment sustainability design and energy efficient resources etc. In this review firstly, a brief discussion on Cloud and Green computing is given then various application areas of Green IT are discussed and reviewed further. Based on the comparative analysis on Green-IT areas several research issues related to concerned Green IT areas, Objectives of such areas etc. are elaborated further.

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Dynamic virtual machine (VM) consolidation is a constructive technique to enhance resource usage and is extensively employed to minimize data centers' energy consumption. However, in the current approaches, consolidation techniques are heavily relied on reducing the actively used physical servers (PMs) based on their current resource utilization without considering future resource demands. Also, many of the reported works for cloud workload prediction applied univariate time series-based forecasting models and neglected the dependency of other resource utilization metrics. Thus, resulting in inaccurate predictions, unnecessary migrations, high migration costs, and increased service level agreement violations (SLAVs) may nullify the consolidation benefits. To efficiently address this issue, we propose a multivariate resource usage prediction-based hotspots and coldspots mitigation approach that considers both the current and future usage of resources with O(sk) time complexity, where s and k denote the number of PMs and VMs, respectively. The proposed technique uses a clustering-based stacked bidirectional (Long Short-Term Memory) LSTM deep learning network to predict the future memory and CPU usage of PMs and VMs with high accuracy and O((Q(Q + W) * Θ) computational complexity, where Q, W, and Θ represent the number of hidden layer cells, outputs, and training epochs, respectively. Through extensive simulations based on Google's cluster workload traces, we demonstrate that our proposed method obtains substantial improvements in terms of prediction performance, energy-efficiency, actively used PMs, VM migrations, and SLA violations over the benchmark approaches.